A novel alignment model inspired on IBM Model 1
Jesús González-Rubio, Germán Sanchis-Trilles, Alfons Juan · 2008
We present an extension to IBM Model 1 for training word-to-word lexicon probabilities. This model takes into account a given fixed segmentation of the source and target sentences in the estimation of the statistical dictionary. Our experimentation on the Europarl corpus shows that a statistical consistent improvement in the translation quality can be achieved by including our proposed model as a new information source in a log-linear combination of models.